Text Mining and Natural Language Processing Frameworks for ‎Enhanced Fake News Detection, Sentiment Analysis, and ‎Automated Summarization in Social Media

  • Authors

    • K. Karnan Research Scholar, Department of Computer Science and Information Science, Annamalai University, Tamil Nadu, India‎
    • Dr.L.R. Aravind Babu Department of Computer Science and Information Science, Annamalai University, Annamalai Nagar, Tamil Nadu, India
    https://doi.org/10.14419/hgj17c14

    Received date: May 6, 2025

    Accepted date: May 18, 2025

    Published date: June 10, 2025

  • Fake News Detection; Sentiment Analysis; Text Summarization; Natural Language Processing (NLP); BiLSTM

  • Abstract

    Efficient text summarization, public sentiment analysis, and fake news detection have become difficult tasks due to the exponential growth of ‎digital content. Sentiment analysis aids in assessing trends and public opinion, while fake news detection is crucial for combating false ‎information. To alleviate information overload, automated text summarization extracts important information from long documents. This ‎study examines three sophisticated Natural Language Processing (NLP) models: 1) The BiLSTM-based sentiment analysis model uses ‎Word2Vec embeddings and bidirectional LSTM units to understand context better and classify text into positive, negative, or neutral ‎sentiments. 2) Followed by a sigmoid classifier, to differentiate real from fake news, the BiLSTM-CNN-based fake news detection model ‎combines a 1D CNN for spatial pattern recognition and BiLSTM for sequential feature extraction. 3) For extractive summarization, the hybrid ‎extractive-abstractive summarization model uses TF-IDF-based sentence weighting for abstractive summarization it uses a Transformer-based encoder-decoder. The outcome is measured using metrics like BLEU and ROUGE. These models improve the online user experience ‎, decision-making, and misinformation detection in text mining applications‎.

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  • How to Cite

    Karnan, K. ., & Babu, D. A. . (2025). Text Mining and Natural Language Processing Frameworks for ‎Enhanced Fake News Detection, Sentiment Analysis, and ‎Automated Summarization in Social Media. International Journal of Basic and Applied Sciences, 14(2), 107-112. https://doi.org/10.14419/hgj17c14